Event Abstract

NeuralEnsemble.Org: Unifying neural simulators in Python to ease the model complexity bottleneck

  • 1 EPFL-LCN, Brain Mind Institute, Switzerland
  • 2 UNIC, CNRS, France
  • 3 University of Heidelberg, Germany
  • 4 Honda Research Institute Europe GmbH, United States
  • 5 BCCN Freiburg, Germany
  • 6 TU Graz, Austria
  • 7 CNRS-MRS, France
  • 8 Freie Universitaet Berlin, Germany

Trends in programming language development and adoption point to Python as the high-level systems integration language of choice. Python leverages a vast developer-base external to the Neuroscience community, and promises leaps in model complexity and maintainability to any neural simulator which adopts it. As such, Python is emerging as the de facto standard language for neural simulator model scripting [1].

We present two projects, PyNN [2] and NeuroTools, hosted at http://neuralensemble.org, which have been concieved to further push the complexity evelope and reduce model development times by promoting code sharing and reuse across simulator communities, and the Python scientific computing community at large.

PyNN provides a unified Python application programming interface (API) in the domain of large-scale networks of point-process neurons and presently supports NEURON, NEST, PCSIM, Brian, and the FACETS VLSI neuromorphic hardware implementation, with support for GENESIS3/MOOSE and NeuroML forthcoming. With PyNN it is possible to write a simulation script once and run it without modification on any supported simulator. A primary goal in developing PyNN was to develop and facilitate development of simulator agnostic higher level modeling concepts in a simulator agnostic way.

NeuroTools is a collection of tools to support all tasks associated with a neural simulation project which are not handled by the simulation engine. NeuroTools is written in Python, and works best with PyNN, or one of the growing list of simulation engines with a Python front-end. NeuroTools provides modules to facilitate simulation setup, parameterization, data management, analysis and visualization. The data-related tools are equally suited to analysis of experimental data, although that is not the primary motivation for their development.

NeuroTools aims to:

1. increase the productivity of individual modellers by automating, simplifying, and establishing best-practices for common tasks,
2. increase the productivity of the Neuroscience modelling community by reducing the amount of code duplication across simulation communities,
3. increase the reliability of data analysis tools leveraging Linus's law: "given enough eyeballs, all bugs are shallow."

NeuroTools and PyNN are open-source softwares by the community for the community, and those interested in using them or contributing to development are invited to visit http://neuralensemble.org.

References

1. R. Koetter, J. Bednar, A. Davison, M. Diesmann, M.-O. Gewaltig, M. Hines, E. Muller (Eds.). (2008). Python in Neuroscience. Frontiers in Neuroinformatics. http://www.frontiersin.org/neuroinformatics/specialtopics/8/.

2. A. P. Davison et al. (2008). PyNN: a common interface for neuronal network simulators. Frontiers in Neuroinformatics. Pending published, http://www.frontiersin.org/neuroinformatics/paper/pending/0/388/.

Conference: Neuroinformatics 2009, Pilsen, Czechia, 6 Sep - 8 Sep, 2009.

Presentation Type: Oral Presentation

Topic: Computational neuroscience

Citation: Muller E, Davison AP, Brizzi T, Bruederle D, Eppler MJ, Kremkow J, Pecevski D, Perrinet L, Schmuker M and Yger P (2009). NeuralEnsemble.Org: Unifying neural simulators in Python to ease the model complexity bottleneck. Front. Neur. Conference Abstract: Neuroinformatics 2009. doi: 10.3389/conf.neuro.11.2009.08.104

Received: 25 May 2009; Published Online: 25 May 2009.

* Correspondence: Eilif Muller, EPFL-LCN, Brain Mind Institute, Lausanne, Switzerland, eilif.mueller@epfl.ch

© 2007 - 2018 Frontiers Media S.A. All Rights Reserved